Denoising of hyperspectral remote sensing imagery using NAPCA and complex wavelet transform

A new denoising algorithm was proposed to keep the fine features of hyperspectral remote sensing imagery effectively. Firstly, the noise-adjust principal components analysis (NAPCA) was performed on the hyperspectral datacube. Then output channels of the low-energy NAPCA were transformed into the wavelet domain by 2-D complex wavelet transform(CWT). The BivaShrink function was used to shrink the wavelet coefficients. And then 1-D CWT denoising method was used to remove the noise of the each spectrum of the low-energy NAPCA datacube. The AVIRIS images Jasper Ridge, Lunar Lake and Low Altitude were used for the simulated experiment. Compared with the HSSNR and the PCABS, the signal-to-noise ratio (SNR) is improved by 4.3-7.8 dB and 0.8-0.9 dB via the proposed method in this paper, which shows that the proposed method is feasible. It is shown that the proposed method is correctable and available according to the experimental results of the real datacube OMIS.